[2J4-GS-10] AI application:

[2J4-GS-10-04] Autonomous Vehicle Control Considering Emergency Response and Recovery in Urban Environments

〇Takuya MANIWA¹, Takeshi KUNIEDA², Sachiyo ARAI² (1. Department of Urban Environment Systems, Graduate School of Science and Engineering, Chiba University, 2. Department of Electrical and Electronic Engineering, Graduate School of Science and Engineering, Chiba University)

Keywords:Autonomous Driving, Emergency Response, Deep Reinforcement Learning

In the introduction of autonomous driving in urban areas, control systems are required to prioritize human lives during emergencies, which differ from normal driving conditions. Control during "emergencies" is typically triggered based on quantified metrics like Time Headway (THW) or Time to Collision (TTC), which reflect the driver's perception of danger while driving. However, in environments where pedestrians, motorcycles, and other vehicles coexist, acquiring appropriate control rules that ensure safety for all entities is extremely challenging. Traditional control theory-based approaches and image-based control methods have inherent limitations in such complex scenarios. Furthermore, in addition to avoiding emergencies, it is necessary to consider the transition back to normal conditions to minimize secondary damage.This study introduces deep reinforcement learning (DRL) for emergencies defined by TTC. It proposes a method that dynamically switches between obstacle avoidance control and recovery control to return to the vehicle's original lane after avoidance, depending on the situation. Experiments conducted in a simulated urban environment demonstrate that the proposed method improves destination arrival accuracy while preventing traffic accidents and maintaining safety.